Base yellowfin tuna model

First, we build the basic tuna model with all the predictors included. Note that the environmental predictors are mean values over 1956-1981.

  1. longitude
  2. latitude
  3. season
  4. temperature (\(^{\circ} C\))
  5. oxygen (\(mol \; m^{-3}\))
  6. pH
  7. chlorophyll (\(mol \; m^{-3}\))
  8. salinity (ppt)
  9. mixed layer thickness (m)
  10. nitrate (\(mol \; m^{-3}\))
  11. phosphate (\(mol \; m^{-3}\))
  12. ammonium (\(mol \; m^{-3}\))
  13. broad-scale thermal gradient (\(\Delta ^{\circ} C \; km^{-1}\))
  14. broad-scale salinity gradient (\(ppt \; km^{-1}\))
  15. eddy kinetic energy (\(m^{-2} \; s^{-2}\))
  16. depth (m)
  17. distance from nearest coast (km)
  18. probability of adult occurrence
## [1] "training AUC: 0.8861"
## [1] "testing AUC: 0.8169"

Then, we extrapolate for the rest of \(40^{\circ}N\)-\(40^{\circ}S\) and present seasonal distribution maps.

January-March

April-June

July-September

October-December

Model without geographical location

## [1] "training AUC: 0.8769"
## [1] "testing AUC: 0.801"

January-March

April-June

July-September

October-December

Increasing confidence of distribution maps.

First, we build the \(10 \times 10\) grid.

Then, we associate the \(10 \times 10\) grid with the \(1 \times 1\) grid cells. This just shows the Jan-March grid. The remaining \(10 \times 10\) grid cells are ones that have sampling points in them.

We visualize this filtered grid overlaid with the distribution map for January-March of model 1 (including all predictors).

Then, we leave only those \(10 \times 10\) grid cells with >25% of its area containing sampling points.

Then, we do this separately per season and replicate this method across all the species.